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(c) CloudPerceptor
Lambda Layers
• AWS Lambda Layers are compressed .zip file archives containing additional
code or data that Lambda functions can leverage. These archives may
include libraries, custom runtimes, data, or configuration files crucial for
Lambda function operations.
• Key Benefits of AWS Lambda Layers:
1.Reduced Code Size:
1. Layers allow for smaller Lambda function code packages, enhancing efficiency in storage and
deployment.
2.Agile Development:
1. Faster iteration on business logic and quicker deployment of code, facilitating more
responsive development cycles.
3.Code Sharing and Reusability:
1. Layers promote the sharing and reuse of code components, fostering collaboration and
efficiency across multiple functions.
4.Dependency Separation:
1. Enables a clear separation between the primary function code and its dependencies,
enhancing modularity and maintainability.
(c) CloudPerceptor
Point to Remember
• You can add maximum layer of 50MB siz
• You can add upto 5 Layers in Lambda Function
• Always create your layer in python folder and make sure zip contains
python folder
•
(c) CloudPerceptor
(c) CloudPerceptor
Lab Overview:
• In this hands-on lab, you'll be guided through the creation of two Lambda layers. The
initial layer will be a Python layer, crafted using both the AWS CLI and AWS Console.
Subsequently, the second layer will be a Node.js layer, defined and deployed through the
AWS Serverless Application Model (SAM) CLI.
• Learning Objectives: By the end of this intermediate-level lab, you will be proficient in:
1. Configuring a Python AWS Lambda Layer via the AWS Console.
2. Configuring a Node.js AWS Lambda Layer using the AWS Serverless Application Model (SAM) CLI.
• Intended Audience: This lab is designed for individuals with the following roles:
• Candidates pursuing the AWS Certified Developer - Associate Certification.
• Cloud Architects.
• Software Engineers.
• Serverless Developers.
• Prerequisites: While not mandatory, familiarity with the following concepts will be advantageous:
• AWS Lambda.
• AWS Serverless Application Model.
(c) CloudPerceptor
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Building Better AWS Lambdas: Unlocking the Power of Layers

  • 2. Lambda Layers • AWS Lambda Layers are compressed .zip file archives containing additional code or data that Lambda functions can leverage. These archives may include libraries, custom runtimes, data, or configuration files crucial for Lambda function operations. • Key Benefits of AWS Lambda Layers: 1.Reduced Code Size: 1. Layers allow for smaller Lambda function code packages, enhancing efficiency in storage and deployment. 2.Agile Development: 1. Faster iteration on business logic and quicker deployment of code, facilitating more responsive development cycles. 3.Code Sharing and Reusability: 1. Layers promote the sharing and reuse of code components, fostering collaboration and efficiency across multiple functions. 4.Dependency Separation: 1. Enables a clear separation between the primary function code and its dependencies, enhancing modularity and maintainability. (c) CloudPerceptor
  • 3. Point to Remember • You can add maximum layer of 50MB siz • You can add upto 5 Layers in Lambda Function • Always create your layer in python folder and make sure zip contains python folder • (c) CloudPerceptor
  • 5. Lab Overview: • In this hands-on lab, you'll be guided through the creation of two Lambda layers. The initial layer will be a Python layer, crafted using both the AWS CLI and AWS Console. Subsequently, the second layer will be a Node.js layer, defined and deployed through the AWS Serverless Application Model (SAM) CLI. • Learning Objectives: By the end of this intermediate-level lab, you will be proficient in: 1. Configuring a Python AWS Lambda Layer via the AWS Console. 2. Configuring a Node.js AWS Lambda Layer using the AWS Serverless Application Model (SAM) CLI. • Intended Audience: This lab is designed for individuals with the following roles: • Candidates pursuing the AWS Certified Developer - Associate Certification. • Cloud Architects. • Software Engineers. • Serverless Developers. • Prerequisites: While not mandatory, familiarity with the following concepts will be advantageous: • AWS Lambda. • AWS Serverless Application Model. (c) CloudPerceptor
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